Background Subtraction using Adaptive Singular Value Decomposition
- PDF / 1,378,301 Bytes
- 14 Pages / 595.276 x 790.866 pts Page_size
- 17 Downloads / 299 Views
Background Subtraction using Adaptive Singular Value Decomposition Günther Reitberger1
· Tomas Sauer1
Received: 4 July 2019 / Accepted: 15 May 2020 © The Author(s) 2020
Abstract An important task when processing sensor data is to distinguish relevant from irrelevant data. This paper describes a method for an iterative singular value decomposition that maintains a model of the background via singular vectors spanning a subspace of the image space, thus providing a way to determine the amount of new information contained in an incoming frame. We update the singular vectors spanning the background space in a computationally efficient manner and provide the ability to perform blockwise updates, leading to a fast and robust adaptive SVD computation. The effects of those two properties and the success of the overall method to perform a state-of-the-art background subtraction are shown in both qualitative and quantitative evaluations. Keywords Image processing · Background subtraction · Singular value decomposition
1 Introduction With static cameras, for example in video surveillance, the background, like houses or trees, stays mostly constant over a series of frames, whereas the foreground consisting of objects of interest, e.g., cars or humans, causes differences in image sequences. Background subtraction aims to distinguish between foreground and background based on previous image sequences and eliminates the background from newly incoming frames, leaving only the moving objects contained in the foreground. These are usually the objects of interest in surveillance.
1.1 Motivation Data-driven approaches are a major topic in image processing and computer vision, leading to state-of-the-art performances, for example in classification or regression tasks. One example is video surveillance used for security reasons, traffic regulation, or as information source in autonomous driving. The main problems with data-driven approaches are that the training data have to be well balanced and to cover all scenarios that appear later in the
B
Günther Reitberger [email protected] Tomas Sauer [email protected]
1
FORWISS, University of Passau, Passau, Germany
execution phase and have to be well annotated. In contrast to cameras mounted at moving objects such as vehicles, static cameras mounted at some infrastructure observe a scenery, e.g., houses, trees, parked cars, that is widely fixed or at least remains static over a large number of frames. If one is interested in moving objects, as it is the case in the aforementioned applications, the relevant data are exactly the one different from the static data. The reduction of the input data, i.e., the frames taken from the static cameras, to the relevant data, i.e., the moving objects, is important for several applications like the generation of training data for machine learning approaches or as input for classification tasks reducing false positive detections due to the removal of the irrelevant static part. Calling the static part background and the movin
Data Loading...
